Adaptive User Profiling in E-Commerce and Administration of Public Services
The World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that use...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/5/144 |
| _version_ | 1827668848170500096 |
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| author | Kleanthis G. Gatziolis Nikolaos D. Tselikas Ioannis D. Moscholios |
| author_facet | Kleanthis G. Gatziolis Nikolaos D. Tselikas Ioannis D. Moscholios |
| author_sort | Kleanthis G. Gatziolis |
| collection | DOAJ |
| description | The World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that uses it, it is difficult to separate and categorize users according to their preferences. One solution to this problem is to create a web-platform that acts as a middleware between end users and the web, in order to analyze the data that is available to them. The method by which user information is collected and sorted according to preference is called ‘user profiling‘. These profiles could be enriched using neural networks. In this article, we present our implementation of an online profiling mechanism in a virtual e-shop and how neural networks could be used to predict the characteristics of new users. The major contribution of this article is to outline the way our <b>online profiles</b> could be beneficial both to customers and stores. When shopping at a traditional <b>physical</b> store, real time targeted “<b>personalized</b>” advertisements can be delivered directly to the mobile devices of consumers while they are walking around the stores next to specific products, which match their buying habits. |
| first_indexed | 2024-03-10T03:52:16Z |
| format | Article |
| id | doaj.art-7cbff6ff0ec447039f7cf22773238a01 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T03:52:16Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-7cbff6ff0ec447039f7cf22773238a012023-11-23T11:04:18ZengMDPI AGFuture Internet1999-59032022-05-0114514410.3390/fi14050144Adaptive User Profiling in E-Commerce and Administration of Public ServicesKleanthis G. Gatziolis0Nikolaos D. Tselikas1Ioannis D. Moscholios2Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, GreeceDepartment of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, GreeceDepartment of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, GreeceThe World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that uses it, it is difficult to separate and categorize users according to their preferences. One solution to this problem is to create a web-platform that acts as a middleware between end users and the web, in order to analyze the data that is available to them. The method by which user information is collected and sorted according to preference is called ‘user profiling‘. These profiles could be enriched using neural networks. In this article, we present our implementation of an online profiling mechanism in a virtual e-shop and how neural networks could be used to predict the characteristics of new users. The major contribution of this article is to outline the way our <b>online profiles</b> could be beneficial both to customers and stores. When shopping at a traditional <b>physical</b> store, real time targeted “<b>personalized</b>” advertisements can be delivered directly to the mobile devices of consumers while they are walking around the stores next to specific products, which match their buying habits.https://www.mdpi.com/1999-5903/14/5/144user profilinge-commerceretailinge-shoppingmobile shoppinganalytics |
| spellingShingle | Kleanthis G. Gatziolis Nikolaos D. Tselikas Ioannis D. Moscholios Adaptive User Profiling in E-Commerce and Administration of Public Services Future Internet user profiling e-commerce retailing e-shopping mobile shopping analytics |
| title | Adaptive User Profiling in E-Commerce and Administration of Public Services |
| title_full | Adaptive User Profiling in E-Commerce and Administration of Public Services |
| title_fullStr | Adaptive User Profiling in E-Commerce and Administration of Public Services |
| title_full_unstemmed | Adaptive User Profiling in E-Commerce and Administration of Public Services |
| title_short | Adaptive User Profiling in E-Commerce and Administration of Public Services |
| title_sort | adaptive user profiling in e commerce and administration of public services |
| topic | user profiling e-commerce retailing e-shopping mobile shopping analytics |
| url | https://www.mdpi.com/1999-5903/14/5/144 |
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